1. Field of the Invention
The present invention relates generally to a method and system for detecting suspected anomalous shadows, and more particularly to a method and system for setting a threshold value matched to the image obtaining environment, and detecting suspected anomalous shadows.
2. Description of the Related Art
In the field of medical radiology//suspected anomalous shadow detection systems, the diagnostic reading of radiation images in order to discover the location of a disease, or to observe the state of a diseased tissue in order to determine the presence or absence of disease or the course of a disease, is widely practiced. However, the results and reliability of such diagnosis depends largely on the experience and skill level of the examiner, and is by no means a purely objective measure.
Take for example a case in which a mammogram (a diagnostic radiation image of the mammary glands) is obtained for the purpose of diagnosing whether or not breast cancer is present. It is necessary to detect the anomalous shadows contained therein that have been cast by tumors and microcalcifications, which are indicative of breast cancer. However, it is not a forgone conclusion that an examiner will be able to correctly specify the exact range defining such anomalous shadows. Therefore, an objective and reliable method and system for detecting anomalous shadows, starting with the shadows of tumors and microcalcifications, which is not dependent on the skill level of the examiner, is called for.
In response to this need, based on the image data of a target subject that has been obtained as a diagnostic image, anomalous shadow detection systems have been proposed that automatically detect anomalous shadows suspected of having been cast by tumors or microcalcifications indicative of breast cancer (hereinafter referred to as suspected anomalous shadows) from the image represented by said image data, by use of a computer (computer assisted image diagnostic apparatus and CAD systems) (see, e.g., Japanese Unexamined Patent Publication Nos. 8(1996)-294479, 8(1996)-287230, etc.).
Such anomalous shadow detection systems detect suspected anomalous shadows based on the pixel values (i.e., image data) that can be considered representative thereof, and display a mark in the areas in which the suspected anomalous shadows occur, so as to alert an examiner of the subject image to positions thereof; alternatively, by quantitatively posting (as numerical data) the characteristic features of the detected suspected anomalous shadows, objectivity in the diagnosis of the subject radiation image is bolstered, and the oversight or misconstrual of the detected suspected anomalous shadows such as those described above is thereby prevented. According to the above-described processing employed in detecting suspected anomalous shadows, an iris filtering process appropriate for detecting mainly suspected anomalous shadows of tumors, a morphology filtering process appropriate for detecting mainly suspected anomalous shadows of microcalcifications, and etc. are utilized.
The expression “iris filtering process” refers to an effective means of detecting regions within an image suspected of containing anomalous shadows of tumors, which are one characteristic feature of breast cancer in mammary glands appearing in a mammogram, by comparing the output value of the iris filter, which represents the largest value of the concentration of the density slopes of the image signal to a predetermined threshold value.
Taking for example a radiation image recorded on an X-ray film (an image represented by a high-density, high signal-level image signal), it is known that the image density of the shadow of a tumor is slightly lower than that of the peripheral region thereof; and a pattern has been recognized wherein the image density slope of the internal area the shadow of a tumor is denser for points toward the substantially circular perimeter and becomes less dense toward the center. Therefore, each point within the shadow of a tumor has a local image density slope pointing to the center of the shadow.
The term “iris filtering” refers to computing as a slope vector the slope of the image-signal, which is represented by the density value, and outputting the concentration of said slope vector; the iris filtering process is the detecting, based on the concentration of the slope vectors, of the suspected regions of the anomalous shadows of tumors.
Hereinafter, referring to the mammogram shown in FIG. 1, an explanation of the iris filtering process will be given. The slope vectors of the pixels within the internal section of a shadow P1 of a tumor occurring in an original image data P, as shown in (2) of FIG. 1, point substantially to the center of the shadow P1 of the tumor, whereas the slope vectors of an elongated anomalous shadows P2, such as that of a blood vessel, a mammary gland, etc., as shown in (3) of FIG. 1, do not point to any particular point. Therefore, if, by evaluating the local distribution of the directions of the slope vectors, a spot is detected toward which the slope vectors therein point (or are concentrated at), said spot can be selected as a suspected region of an anomalous shadow of a tumor. Further, a shadow P3 of the point at which two elongated shadows of mammary glands, etc. cross, as shown in (4) of FIG. 1, is detected as a pseudo-suspected region because there is a tendency for the slope vectors thereof to point to a particular point.
Hereinafter, an algorithm for the iris filtering process will be shown.
First, the direction θ representing the orientation of the slope vector of the image data is computed for each pixel j forming the subject image using the equation (1) below.
                    θ        =                              tan                          -              1                                ⁢                                                                                                                (                                                                        f                          3                                                +                                                  f                          4                                                +                                                  f                          5                                                +                                                  f                          6                                                +                                                  f                          7                                                                    )                                        -                                                                                                                    (                                                                  f                        11                                            +                                              f                        12                                            +                                              f                        13                                            +                                              f                        14                                            +                                              f                        15                                                              )                                                                                                                                                                  (                                                                        f                          1                                                +                                                  f                          2                                                +                                                  f                          3                                                +                                                  f                          15                                                +                                                  f                          16                                                                    )                                        -                                                                                                                    (                                                                  f                        7                                            +                                              f                        8                                            +                                              f                        9                                            +                                              f                        10                                            +                                              f                        11                                                              )                                                                                                          (        1        )            
Here, each of f1–f16, as shown in FIG. 2, represent a pixel value (i.e., image data) corresponding to one of the outermost pixels of a mask of a 5×5 pixels centered on the pixel j.
Next, the concentration C of the slope vectors toward the pixel of interest is computed for each pixel forming the subject image, taking that pixel as the pixel of interest, by using the following equation (2).
                    C        =                              (                          1              /              N                        )                    ⁢                                    ∑                              j                =                1                            N                        ⁢                                                  ⁢                          cos              ⁢                                                          ⁢                              θ                j                                                                        (        2        )            
In the equation (2), N represents the number of pixels within a circle having a radius R centered on a pixel of interest, and θ is the angle formed between the slope vector computed according to aforementioned equation (1) and the linear line connecting the pixel of interest located at the center of said circle and each pixel j within said circle (see FIG. 3). Accordingly, the concentration C computed according to aforementioned equation 2 becomes a large value when the slope vector of each pixel points toward the pixel of interest.
The slope vector of a pixel j would point substantially to the center of the shadow regardless of the degree of contrast of the shadow, if the pixel j resided near the shadow of the tumor. Thus, it can be assumed that a pixel of interest for which the concentration C is of a large value would be a pixel near the center of the shadow of the tumor. On the other hand, because the slope vectors within the shadow of a blood vessel or other elongated shadow tend to have substantially parallel directions, the concentration C thereof is small. Accordingly, the shadow of the tumor can be appropriately detected by calculating the value of the concentration C for each pixel constituting the image to be analyzed taking that pixel as the pixel of interest and thereafter checking whether or not the calculated value of the concentration C is larger than the predetermined threshold value. As described above, the iris filter is less likely to erroneously detect the shadow of a blood vessel, a mammary gland, etc. than a regular differential filter, and thus has the advantage of detecting the shadow of a tumor more effectively.
The iris filter used in the actual detection process is preferably configured so that the size and shape of an actual portion thereof may be arbitrarily changed to maintain stable detection capability regardless of the size and shape of the tumor. FIG. 4 shows an example of such a filter, which is different from that shown in FIG. 3. The filter shown in FIG. 4 evaluates the values of the concentration C only for those pixels on predetermined lines extending radially from the pixel of interest. The angle between any two adjacent radial lines is 2π/M degrees, wherein M represents the number of radial lines. In FIG. 4, M=32 is assumed, and thus the angle between any two adjacent lines is 11.25 degrees.
A set of coordinates ([x], [y]) for the nth pixel on the ith radial line is given by the following equations (3) and (4):x=k+n cos {2π(i−1)/M }  (3)y=l+n sin {2π(i−1)/M }  (4)wherein the coordinates of the pixel of interest are designated as (K,l), and the coordinates [x], [y] are the largest integers that do not exceed the values of the coordinates x, y, respectively.
The concentration of the slope vectors within the first n pixels on a radial line i toward the pixel of interest is obtained for several values of n, and the maximum value thereof, Cimax, is selected on a line-by-line basis. Then, the average value of Cimax over every radial line i is taken as the concentration C of the slope vectors toward the pixel of interest.
More specifically, the concentration Ci (n) is first obtained for several values of n using the following equation (5):
                                          Ci            ⁡                          (              n              )                                =                                    ∑                              i                =                1                            n                        ⁢                                                  ⁢                          {                                                (                                      cos                    ⁢                                                                                  ⁢                                          θ                      i1                                                        )                                /                n                            }                                      ,                  Rmin          ≦          n          ≦          Rmax                                    (        5        )            
Derived from the equation (5) is the concentration of the slope vectors within the first n pixels on a radial line i toward the pixel of interest, taking one integer within the range of Rmin≦n≦Rmax as the value of n.
Herein, Rmin to Rmax stand for the minimum radius and the maximum radius for the suspected shadow of the tumor.
Based on each value of Ci(n) calculated using the equation (5), the concentration C of the slope vectors toward the pixel of interest is computed using the following equations (6) and (7):
                              Ci          max                =                              max                          Rmin              ≦              n              ≦              Rmax                                ⁢                      Ci            ⁡                          (              n              )                                                          (        6        )                                C        =                              (                          1              /              32                        )                    ⁢                                    ∑                              i                =                1                            32                        ⁢                                                  ⁢                          Ci              max                                                          (        7        )            
As each Cimax obtained using the equation (6) is the maximum value of the concentration Ci (n) (Rmin≦n≦Rmax) obtained for the radial line i using the equation (5), the dimension between the pixel of interest and the pixel for which the Ci (n) thereof has been selected as Cimax may be considered as a radius of the suspected shadow of the tumor in the direction of the radial line.
The periphery of the suspected anomalous shadow of the tumor is defined by a set of straight lines or non-linear curves connecting each pixel having the concentration Ci thereof selected as the Cimax on each radial line i (i.e., I=1, 2, . . . , 32).
Then, by using equation (7), the concentration C of the slope vectors toward the pixel of interest is obtained by taking the average value of Cimax over every radial line i (herein, the number of radial lines is 32). This concentration C would be the output value I of the iris filter. Then the output value I of the iris filter is compared to the predetermined threshold value T. If I≧T (or I>T), the area centering on that pixel of interest would remain as the suspected shadow of a tumor, and if I≦T (or I<T), the area would be regarded as a normal area, and another candidate area would be searched for.
In the above process, the following equation (5′) for calculating each Ci(n) may substitute for the equation (5).
                                          Ci            ⁡                          (              n              )                                =                                    1                              n                -                Rmin                +                1                                      ⁢                                          ∑                                  i                  =                  Rmin                                n                            ⁢                                                          ⁢                              cos                ⁢                                                                  ⁢                                  θ                  i1                                                                    ,                  Rmin          ≦          n          ≦          Rmax                                    (                  5          ′                )            
Taking one integer within the range of Rmin≦n≦Rmax as the value of n, the concentration of the slope vectors within Rminth to nth pixels on a radial line i toward the pixel of interest is obtained by the equation (5′).
That is to say, equation (5′) computes concentration Ci(n) within an area defined by utilizing the pixel corresponding to the shortest radius Rmin of the radius of the suspected anomalous shadow of a tumor for which a detection is being performed as the starting point, and a point in the range from Rmin to Rmax as the finishing point.
On the other hand, a morphology filtering process is a process for detecting suspected microcalcification shadows, which, like the shadow of a tumor, is a characteristic feature of breast cancer. The morphology filtering process determines the suspected microcalcification shadows based on the use of a multiscale λ and a structural factor (a mask) B, and is advantageous in that: (1) the process is effective in extracting the shadows that are in fact shadows of microcalcifications; (2) the process is less susceptible to producing erroneous readings due to effects caused by complex background information; and (3) the extracted images of microcalcifications are free from distortion, etc. That is to say, microcalcification shadows detected using the morphology filtering process reflect geometric features (e.g., the size, shape, or image density distribution) of the actual microcalcification shadows more precisely than those shadows detected by using general differentiation processes. A general outline of the steps constituting a morphology filtering process will be given below.
Basic Morphology Calculation
A process of morphology generally consists of a series of set operations in an N-dimensional space. However, the process explained hereunder is directed to a two-dimensional monotone image for the sake of simplicity.
The two-dimensional monotone image is regarded as a three-dimensional space constituted of a certain number of coordinate points, (x, y), having respective heights corresponding to an image density signal f(x, y) thereof. In the present description, an image density signal f(x, y) of a higher level represents higher brightness, i.e., lower image density.
Now, for further simplicity, a linear function f(x) corresponding to the image density signal along a strip of the image will be considered. A structuring element used in the morphology calculation is a symmetric function with respect to the zero-point, i.e.,gS(x)=g(−x)  (8)Wherein the value is equal to 0 within a defined domain, said domain being:G={−m, −m+1, . . . , −1, 0, 1, . . . , m−1, m}  (9)
Using the above structuring element g, basic forms of morphology operations can be written in quite simple forms as shown below.dilation: └f⊕GS┘(i)=max{f(i−m), . . . , f(i), . . . , f(i+m)}  (10)erosion: [f⊖GS](i)=min{f(i−m), . . . , f(i), . . . , f(i+m)}  (11)opening: fg=(f⊖gS)⊕g  (12)closing: fg=(f⊕gS)⊖g  (13)
Herein, the dilation operation is an operation of searching for the maximum value within a range of ±m (a value determined by structural element B), centered on a pixel of interest (see FIG. 5A). The erosion operation is an operation of searching for the minimum value within a range of ±m centered on a pixel of interest (see FIG. 5B). The opening operation is an operation of searching for the minimum value first and then searching for the maximum value. The closing operation is an operation of searching for the maximum value first and then searching for the minimum value. More specifically, the opening operation smoothes the image density distribution on the low-brightness side thereof to filter out up-pointing peaks, (i.e., those parts with higher brightness and thus lower image density than adjacent areas thereof) which occur within a range spatially narrower than the present example (see FIG. 5C). The mask size is 2 meters in the present example. On the other hand, the closing operation smoothes the image density distribution on the high-brightness side thereof to filter out down-pointing peaks (i.e., those parts with lower brightness and thus higher image density than adjacent areas thereof) which occur within a range spatially narrower than a mask size of 2 m(see FIG. 5D).
For cases in which the image signal f(x, y) of higher level represents higher image density, because the size relationship between the image density value f(x) and the image signal would be reversed from the case in which the higher the brightness, the higher the signal, the dilation operation would be the operation identical to the above-described erosion operation (see FIG. 5B) and the erosion operation would be the operation identical to the above-described dilation operation (see FIG. 5A). Similarly, the opening operation would be the operation identical to the above-described closing operation (see FIG. 5D) and the closing operation would be the same operation identical to the above-described opening operation (see FIG. 5C). In the following descriptions, however, as well as in the above description of the morphology filtering process, the image density signal f(x, y) of higher level is assumed to represent higher brightness.
Application to Calcification Shadow Detection
There has been a conventional subtraction method for detecting a calcification shadow in which a smoothed image is subtracted from an original image. However, a simple smoothing method is incapable of precisely distinguishing a calcification shadow from an elongated normal shadow (e.g., a shadow of a mammary gland, a mammary gland support tissue, a blood vessel, etc.). To overcome this problem a morphology operation represented by the following equation (14), which is based on an opening operation using a structuring element B, was proposed in Obata's papers, each titled “Extraction of Microcalcifications on Mammogram Using Morphological Filter with Multiple Structuring Elements” (Electronic Information Communication Society Journal, D-II, Vol. J75-D-II, No. 7, p. 1170–1176, 7/1992) and “Basics of Morphology Operation and its Application to Mammogram” (Medical Imaging Technology, Vol. 12 No. 1, 1/1994).
                                                        P              =                              f                -                                                      max                                          i                      ∈                                              (                                                  1                          ,                                                                                                          ⁢                          …                          ⁢                                                                                                          ,                          M                                                )                                                                              ⁢                                      {                                                                  (                                                  f                          ⊖                          Bi                                                )                                            ⊕                      Bi                                        }                                                                                                                          =                              f                -                                                      max                                          i                      ∈                                              (                                                  1                          ,                                                                                                          ⁢                          …                          ⁢                                                                                                          ,                          M                                                )                                                                              ⁢                                      {                                          f                      Bi                                        }                                                                                                          (        14        )            
In the above equation (14), Bi(I=1, 2, 3, 4) represents one of the four linear dimensions of the structuring element B as shown in FIG. 6. When selecting lines longer than respective dimensions of the target calcification shadow as the linear dimensions Bi and conducting the opening operation, the target calcification shadow will be regarded as an up-pointing peak of the image density signal which is narrower in each direction corresponding to each Bi than the structuring element B and thus will be filtered out. On the other hand, an elongated normal shadow, which usually has a dimension longer than that of structuring element B in one or more directions corresponding to directions of Bi, still remains after the opening operation (i.e., the operation represented by the second term of the equation (14)). Accordingly, an image containing only those shadows strongly suspected to be calcification shadows will be derived by subtracting the smoothed image, which is obtained through the opening operation and contains no calcification shadow, from an original image f.
Note that for cases in which the image density signal f(x), y) of higher level represents higher image density, i.e., where the closing operation is used in place of the opening operation, the following equation (15) may substitute for the equation (14).
                                                        P              =                              f                -                                                      min                                          i                      ∈                                              (                                                  1                          ,                                                                                                          ⁢                          …                          ⁢                                                                                                          ,                          M                                                )                                                                              ⁢                                      {                                                                  (                                                  f                          ⊕                          Bi                                                )                                            ⊖                      Bi                                        }                                                                                                                          =                              f                -                                                      min                                          i                      ∈                                              (                                                  1                          ,                                                                                                          ⁢                          …                          ⁢                                                                                                          ,                          M                                                )                                                                              ⁢                                      {                                          f                      Bi                                        }                                                                                                          (        15        )            
However, the operation represented by equation (14) or (15) cannot eliminate a normal shadow having a size substantially the same as that of the target calcification shadow. Thus, the additional morphology calculation described by equation (16) is carried out to obtain differential data Mgrad used for eliminating such small normal shadows.Mgrad=(½)×{⊕λB−f⊖λB}  (16)
As a spot having larger value of Mgrad is more likely to be a calcification shadow, a suspected calcification shadow may be determined using the following criteria.IF P(i,j)≧T1 and Mgrad(i,j)≧T2  (17)Then Cs(i,j)=P else Cs(i,j)=0
In the above equation (17), T1 and T2 represent threshold values predetermined empirically.
However, for cases in which the size of the normal shadow is different from that of the target calcification shadow, because the normal shadow can be eliminated by performing only a comparison of the P of the equation (14) and the predetermined threshold value T1, it is sufficient that only the first criterion of the equation (17) (P (i, j)≧T1) be satisfied for cases in which there are no normal shadows having the same size as the target calcification shadow remaining.
Finally, as shown in the equation (18), by combining the multiscale opening and closing calculations, microcalcification clusters Cc are detected.Cc=CS⊕λ1B⊖λ3B⊖λ2B  (18)
In the above equation (18), λ1 and λ2 are determined by the greatest distance between the microcalcification shadows that are desired to be fused, and the largest radius of isolated microcalcifications shadows that are desired to be eliminated, respectively, and λ3=λ1+λ2.
Note that as described above, the explanation relating to the morphology filters has been conducted for a case in which high-brightness, high signal level image data has been the subject of processing, however, for cases in which the image data to be processed is high-density, high signal level image data (image data having higher digital values as the density of the pixels are higher), the relationship between the opening calculation and the closing calculation is reversed.
When an anomalous shadow detection processing system is introduced into a hospital or other facility, the detection threshold values (detection parameters) T, T1 and T2 utilized in performing the above-described morphology processing and anomalous shadow detection processing are experimentally determined and set in advance as initial values.
However, because the detection results obtained by use of the anomalous shadow detection processing system described above are susceptible to fluctuations due to factors in the image-obtaining environment of said facility, if the detection parameters are set as particular values, there is a possibility that the results obtained will vary for each facility. Therefore, when introducing an anomalous shadow detection processing system, in order to facilitate stable detection capacity across all facilities in which such systems are in use, it is necessary that the detection parameters are matched to the image-obtaining environment of each facility and set.